How Dirty Is Your Data?

Picture this scenario: It’s 3:30 on Friday, and the mail arrives on your desk. On top of the stack is an audit notice, requesting six months of records for three clients at your ABA practice. How do you feel? Are you in full panic mode, the weekend now devoted to hunting down and reconciling all files to provide everything that’s required? Or are you calm and confident, knowing your systems can easily pull what’s needed (and that the work can quickly be handled on Monday)?

For many ABA leaders, this hypothetical scenario could easily become a real-life headache, requiring days of admin work across multiple staff members.

Or, with the right preparation, that cool, collected leader could be you. It all starts with good data governance.

Take an Honest Assessment of Your ABA Practice Data

Like most ABA practices, your organization has plenty of data. But how aware are you (and your staff) of what you have at hand?

Data awareness is often low, across all levels of an organization. Not surprising given how busy ABA practices are, with BCBAs and RBTs focused on clients, session notes, and follow-up reports. The majority of billing staff time is devoted to getting claims paid. A clinic may not have a full-time data analyst (or even a contractor) to provide insights. All together, it leaves little time to stay on top of what’s being collected and filed, much less analyze long-term trends or patterns.

Out of necessity, staff often develop workarounds that impact data. They may wait until the end of the day to record session notes, relying on memory, and miss key points in capturing client progress. Colleagues may use different terms or codes (instead of a universal standard) across a client file, leading to potential billing and/or compliance issues. Over time, those workarounds add up. Made thousands of times a week, across dozens of employees, those small decisions either build a foundation your practice can stand on, or they quietly erode it.

Luckily, ABA leaders can take proactive steps to improve their systems and organizational decision-making, without having to overhaul everything or start from scratch.

What “Dirty Data” Actually Looks Like

Problematic data can be tricky to identify, because it’s often not flashy or overt. It doesn’t necessarily trigger alerts or get flagged as an error. For many ABA organizations, it’s just part of the typical workflow, accepted as normal because it’s just always been part of how things get done.

Simply put, dirty data looks like business as usual.

So, how can you determine the state of your data? Here are a few honest questions for you and your team to consider:

• Do two staff members ever describe the same client’s progress differently, using different terms for the same behavior?
• How often does a claim bounce back for a documentation gap, requiring an employee to chase down verification after the fact?
• If you pulled a random session note from three months ago, would it stand up to an audit without someone needing to “remember what really happened”?
• Are your outcome trends built on consistent measurement, or more on whatever each clinician happened to record that day?

Remember: This simple assessment aims to be diagnostic, the first step toward improvements. If two or three of the above questions made you pause, that’s useful information, pointing exactly to where your data needs attention.

Why Clean Data Matters (Even More Than It Used To)

ABA has always run on data. What’s changed in recent years is how much now depends on that data being accurate.

Payers are asking harder questions before they pay a claim. Regulators expect documentation that holds up months later, not just on the day it was written. As more ABA practices lean on automation and AI to lighten administrative loads, those tools are only as good as the information they contain. An algorithm isn’t able to tell the difference between a real trend and a typo, for example. It just processes what it’s given.

With a growing number of innovations in AI-driven scheduling, billing, and clinical support now available, it’s more critical than ever that an ABA practice’s underlying data is trustworthy. Clean data is the difference between a tool that helps and a tool that creates more work. (Or, to borrow an expression from computer science, garbage in equals garbage out.)

Building Habits that Keep Data Clean

Like any worthwhile change, fostering clean data across a practice starts with small, manageable adjustments: in other words, healthy behaviors that can be sustained long term. Give the following five clean data habits a try this month, with an aim to make them stick.

Clean Data Habit #1: Standardize the language. Agree, as a team, on how key behaviors and outcomes get described and measured. Consistency in, consistency out.

Clean Data Habit #2: Catch errors close to the source. The earlier a documentation gap or coding mismatch is flagged, the cheaper and easier it is to fix. Waiting until claims and/or audits come in will likely be the most expensive way to address issues.

Clean Data Habit #3: Make data entry part of the workflow, not a separate task. When documentation happens naturally in the moment, accuracy tends to follow. When it’s treated as paperwork tacked onto the end of a long day, that’s where mistakes creep in.

Clean Data Habit #4: Be proactive with audits. A regular, low-stakes check-in on data quality catches drift early and avoids panic later. Waiting for an external audit to surface problems means you’re making (potentially unpleasant) discoveries on someone else’s timeline.

Clean Data Habit #5: Ask what your systems are actually built to support. Some platforms are built to unify documentation, billing, and scheduling around a single, consistent record. Others leave that unification up to your team, one workaround at a time. Knowing which type you’re working with can save you time and money later.

Make Clean Data a Priority

Clean data won’t fix a broken practice on its own, and dirty data won’t sink a well-run one overnight. But over months and years, that gap compounds.

Let’s picture a new scenario: Imagine a typical afternoon at your practice today, then think of a similar day, but one year from now. Do your clinicians have more time for clients, or are they burned out over paperwork? As a leader, do you have more clarity into trends and gaps across the organization, or are there still (costly) unknowns? Are families satisfied with your services or growing increasingly frustrated?

Clean data can go a long way toward alleviating the pains that come from everyday operational challenges, while warding off future problems as well. And making improvements doesn’t have to take a lot of effort.

You already know your practice better than any audit does. Take 15 minutes this week, run through the questions above, and see what they turn up. You’ll then have a roadmap for where to start cleaning up your data.

So when the next audit request comes in, you’ll find there’s no need to panic.

RethinkBH offers intelligent solutions so your practice can streamline operations, support staff, and deliver better care. Get a closer look by requesting a demo.